Highly accurate protein structure prediction with AlphaFold

Although AlphaFold has a high accuracy across the vast majority of deposited PDB structures, we note that there are still factors that affect accuracy or limit the applicability of the model. The model uses MSAs and the accuracy decreases substantially when the median alignment depth is less than around 30 sequences (see Fig. 5a for details). We observe a threshold effect where improvements in MSA depth over around 100 sequences lead to small gains. We hypothesize that the MSA information is needed to coarsely find the correct structure within the early stages of the network, but refinement of that prediction into a high-accuracy model does not depend crucially on the MSA information. The other substantial limitation that we have observed is that AlphaFold is much weaker for proteins that have few intra-chain or homotypic contacts compared to the number of heterotypic contacts (further details are provided in a companion paper). This typically occurs for bridging domains within larger complexes in which the shape of the protein is created almost entirely by interactions with other chains in the complex. Conversely, AlphaFold is often able to give high-accuracy predictions for homomers, even when the chains are substantially intertwined (Fig. 5b). We expect that the ideas of AlphaFold are readily applicable to predicting full hetero-complexes in a future system and that this will remove the difficulty with protein chains that have a large number of hetero-contacts.

Related work

The prediction of protein structures has had a long and varied development, which is extensively covered in a number of reviews. Despite the long history of applying neural networks to structure prediction, they have only recently come to improve structure prediction. These approaches effectively leverage the rapid improvement in computer vision systems by treating the problem of protein structure prediction as converting an ‘image’ of evolutionary couplings to an ‘image’ of the protein distance matrix and then integrating the distance predictions into a heuristic system that produces the final 3D coordinate prediction. A few recent studies have been developed to predict the 3D coordinates directly, but the accuracy of these approaches does not match traditional, hand-crafted structure prediction pipelines. In parallel, the success of attention-based networks for language processing and, more recently, computer vision has inspired the exploration of attention-based methods for interpreting protein sequences.